Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas

Radiology. 2020 Jul;296(1):216-224. doi: 10.1148/radiol.2020192764. Epub 2020 May 12.


Background Deep learning models have the potential for lung cancer prognostication, but model output as an independent prognostic factor must be validated with clinical risk factors. Purpose To develop and validate a preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinoma. Materials and Methods In this retrospective study, a deep learning model was trained to extract prognostic information from preoperative CT examinations. Data set 1 for training, tuning, and internal validation consisted of patients with T1-4N0M0 adenocarcinoma resected between 2009 and 2015. Data set 2 for external validation included patients with clinical T1-2aN0M0 (stage I) adenocarcinomas resected in 2014. Discrimination was assessed by using Harrell C index and benchmarked against the clinical T category. The Greenwood-Nam-D'Agostino test was used for model calibration. The multivariable-adjusted hazard ratios (HRs) were analyzed with clinical prognostic factors by using the Cox regression. Results Evaluated were 800 patients (median age, 64 years; interquartile range, 56-70 years; 450 women) in data set 1 and 108 patients (median age, 63 years; interquartile range, 57-71 years; 60 women) in data set 2. The C indexes were 0.74-0.80 in the internal validation and 0.71-0.78 in the external validation, both comparable with the clinical T category (0.78 in the internal validation and 0.74 in the external validation; all P > .05). The model exhibited good calibration in all data sets (P > .05). Multivariable Cox regression revealed that model outputs were independent prognostic factors (hazard ratio [HR] of the categorical output, 2.5 [95% confidence interval {CI}: 1.03, 5.9; P = .04] in the internal validation and 3.6 [95% CI: 1.6, 8.5; P = .003] in the external validation). Other than the deep learning model, only smoking status (HR, 3.4; 95% CI: 1.4, 8.5; P = .007) contributed further to prediction of disease-free survival for patients after resection of clinical stage I adenocarcinomas. Conclusion A deep learning model for chest CT predicted disease-free survival for patients undergoing an operation for clinical stage I lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma of Lung / diagnostic imaging*
  • Aged
  • Deep Learning
  • Disease-Free Survival
  • Female
  • Humans
  • Lung / diagnostic imaging
  • Lung Neoplasms / diagnostic imaging*
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Preoperative Care / methods*
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Retrospective Studies
  • Tomography, X-Ray Computed / methods*